Day 1

Session 1: Introduction to Natural Language Processing (NLP)

• Overview of NLP and its applications in conversational AI
• Key components: syntax, semantics, and context
• Evolution of NLP in AI-driven technologies

Session 2: Text Processing and Preprocessing Techniques

• Tokenization, stemming, and lemmatization
• Removing stop words, handling punctuation, and text normalization
• Hands-on Exercise: Preprocessing raw text data

Session 3: Hands-On Lab – Building Basic NLP Pipelines

• Introduction to NLP libraries (NLTK, spaCy)
• Implementing basic NLP pipelines for text analysis
• Real-time testing with sample datasets

Day 2

Session 1: Language Models and Vectorization Techniques

• Understanding word embeddings (Word2Vec, GloVe)
• TF-IDF and Bag of Words models
• Hands-on Exercise: Creating vector representations for text data

Session 2: Intent Recognition and Entity Extraction

• Identifying user intents and extracting entities from conversations
• Techniques for training intent classification models
• Hands-on Exercise: Developing an intent recognition system

Session 3: Hands-On Lab – Integrating NLP Models into Chatbots

• Designing conversational flows powered by NLP
• Building a basic chatbot with integrated NLP capabilities
• Testing chatbot interactions for accuracy and relevance

Day 3

Session 1: Advanced NLP Techniques

• Named Entity Recognition (NER) and sentiment analysis
• Introduction to transformer models (BERT, GPT)
• Ethical considerations in NLP (bias, fairness, and data privacy)

Session 2: Optimizing Conversational AI with NLP

• Performance evaluation metrics for NLP models
• Techniques for improving model accuracy and efficiency
• Hands-on Exercise: Optimizing chatbot responses using NLP algorithms

Session 3: Hands-On Lab – Capstone Project

• Designing an intelligent conversational agent using NLP techniques
• End-to-end project: From data preprocessing to chatbot deployment
• Group presentations and feedback session